118 citations · 167 across the 15 of their papers we have counts for
15 papers
Foundation Time-Series AI Model for Realized Volatility Forecasting
Anubha Goel, Puneet Pasricha, Martin Magris +1
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. These models are trained on numerous diverse datasets and claim to be…
Variational Inference for GARCH-family Models
Martin Magris, Alexandros Iosifidis
The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust appr…
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots
Mostafa Shabani, Martin Magris, George Tzagkarakis +2
Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchroniz…
Bayesian Learning for Neural Networks: an algorithmic survey
Martin Magris, Alexandros Iosifidis
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topic and the multitude of ingredients involved therein, besides the complexity of t…
Manifold Gaussian Variational Bayes on the Precision Matrix
Martin Magris, Mostafa Shabani, Alexandros Iosifidis
We propose an optimization algorithm for Variational Inference (VI) in complex models. Our approach relies on natural gradient updates where the variational space is a Riemann mani…
Quasi Black-Box Variational Inference with Natural Gradients for Bayesian Learning
Martin Magris, Mostafa Shabani, Alexandros Iosifidis
We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework for effi…
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets
Martin Magris, Mostafa Shabani, Alexandros Iosifidis
The prediction of financial markets is a challenging yet important task. In modern electronically-driven markets, traditional time-series econometric methods often appear incapable…
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction
Mostafa Shabani, Dat Thanh Tran, Martin Magris +2
Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis. This is mostly due to the highly non-stationary and noisy nature of f…
On the simulation of the Hawkes process via Lambert-W functions
Martin Magris
Several methods have been developed for the simulation of the Hawkes process. The oldest approach is the inverse sampling transform (ITS) suggested in \citep{ozaki1979maximum}, but…
A Vine-copula extension for the HAR model
Martin Magris
The heterogeneous autoregressive (HAR) model is revised by modeling the joint distribution of the four partial-volatility terms therein involved. Namely, today's, yesterday's, last…
Option market (in)efficiency and implied volatility dynamics after return jumps
Juho Kanniainen, Martin Magris
In informationally efficient financial markets, option prices and this implied volatility should immediately be adjusted to new information that arrives along with a jump in underl…
Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis
Martin Magris, Jiyeong Kim, Esa Rasanen +1
Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultra-high freque…
Implied volatility smile dynamics in the presence of jumps
Martin Magris, Perttu Barholm, Juho Kanniainen
The main purpose of this work is to examine the behavior of the implied volatility smiles around jumps, contributing to the literature with a high-frequency analysis of the smile d…
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Dat Thanh Tran, Martin Magris, Juho Kanniainen +2
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders…
Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods
Adamantios Ntakaris, Martin Magris, Juho Kanniainen +2
Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the i…